Course List

This course is designed for graduate students intending to pursue political methodology as a major field. It covers advanced statistical methods that are not yet standard fare in political methodology courses: e.g., semiparametric methods, nonparametric regression, time-series econometrics, Bayesian methods, and ideal point estimation. Course content will vary year to year, and this semester will focus more heavily on Bayesian methods, simulation-based estimation, and ideal point estimation. As a research workshop, this course also allows students to pursue areas of individual interest in more depth, and therefore course content is determined based on the interests of both the professor and the students. Prerequisites: PSC 404, PSC 405, and PSC 505.

This course covers advanced statistical methods that go beyond linear models and maximum likelihood estimation. Course content will vary year to year and will be determined by the interests of the students and the instructor. Typical topics will include Bayesian markov chain monte carlo methods, ideal point estimation, non-parametric and semi-parametric estimation, causal inference, and machine learning techniques. As a research workshop, this course also allows students to pursue areas of individual interest in more depth. Students are assumed to have taken graduate courses in mathematical probability and inference (PSC 404), linear models (PSC 405), and maximum likelihood estimation (PSC 505). Students will be expected to know how to program their own estimators in R.

This course covers advanced statistical methods that go beyond linear models and maximum likelihood estimation. Course content will vary year to year and will be determined by the interests of the students and the instructor. Typical topics will include Bayesian markov chain monte carlo methods, ideal point estimation, non-parametric and semi-parametric estimation, causal inference, and machine learning techniques. As a research workshop, this course also allows students to pursue areas of individual interest in more depth. Students are assumed to have taken graduate courses in mathematical probability and inference (PSC 404), linear models (PSC 405), and maximum likelihood estimation (PSC 505). Students will be expected to know how to program their own estimators in R.

This course is designed for graduate students intending to pursue political methodology as a major field. It covers advanced statistical methods that are not yet standard fare in political methodology courses: e.g., semiparametric methods, nonparametric regression, time-series econometrics, Bayesian methods, and ideal point estimation. Course content will vary year to year, and this semester will focus more heavily on Bayesian methods, simulation-based estimation, and ideal point estimation. As a research workshop, this course also allows students to pursue areas of individual interest in more depth, and therefore course content is determined based on the interests of both the professor and the students. Prerequisites: PSC 404, PSC 405, and PSC 505.

This course is designed for graduate students intending to pursue political methodology as a major field. It covers advanced statistical methods that are not yet standard fare in political methodology courses: e.g., semiparametric methods, nonparametric regression, time-series econometrics, Bayesian methods, and ideal point estimation. Course content will vary year to year, and this semester will focus more heavily on Bayesian methods, simulation-based estimation, and ideal point estimation. As a research workshop, this course also allows students to pursue areas of individual interest in more depth, and therefore course content is determined based on the interests of both the professor and the students. Prerequisites: PSC 404, PSC 405, and PSC 505.

This course is designed for graduate students intending to pursue political methodology as a major field. It covers advanced statistical methods that are not yet standard fare in political methodology courses: e.g., semiparametric methods, nonparametric regression, time-series econometrics, Bayesian methods, and ideal point estimation. Course content will vary year to year, and this semester will focus more heavily on Bayesian methods, simulation-based estimation, and ideal point estimation. As a research workshop, this course also allows students to pursue areas of individual interest in more depth, and therefore course content is determined based on the interests of both the professor and the students. Prerequisites: PSC 404, PSC 405, and PSC 505.

This course is designed for graduate students intending to pursue political methodology as a major field. It covers advanced statistical methods that are not yet standard fare in political methodology courses: e.g., semiparametric methods, nonparametric regression, time-series econometrics, Bayesian methods, and ideal point estimation. Course content will vary year to year, and this semester will focus more heavily on Bayesian methods, simulation-based estimation, and ideal point estimation. As a research workshop, this course also allows students to pursue areas of individual interest in more depth, and therefore course content is determined based on the interests of both the professor and the students. Prerequisites: PSC 404, PSC 405, and PSC 505.